A novel model for cyanobacteria bloom formation: the critical role of anoxia and ferrous iron
Bibliographic record
Abstract
Summary A novel conceptual model linking anoxia, phosphorus ( P ), nitrogen ( N ), iron (Fe) and sulphate to the formation of noxious filamentous and colonial cyanobacteria blooms is presented that reconciles seemingly contradictory ideas about the roles of P , N and Fe in bloom formation. The model has several critical concepts: (i) P regulates biomass and productivity in fresh waters until excessive loading renders a system N‐limited or light‐limited, but it is the availability of ferrous ions (Fe 2+ ) that regulates the ability of cyanobacteria to compete with its eukaryotic competitors; (ii) Fe 2+ diffusing from anoxic sediments is a major Fe source for cyanobacteria, which acquire it by migrating downwards into Fe 2+ ‐rich anoxic waters from oxygenated waters; and (iii) subsequent cyanobacterial siderophore production provides a supply of Fe 3+ for reduction at cyanobacteria cell membranes that leads to very low Fe 3+ concentrations in the mixing zone. When light and temperature are physiologically suitable for cyanobacteria growth, bloom onset is regulated by the onset of internal Fe 2+ loading which in turn is controlled by anoxia, reducible Fe content of surface sediments and sulphate reduction rate. This conceptual model provides the basis for improving the success of approaches to eutrophication management because of its far‐reaching explanatory power over the wide range of conditions where noxious cyanobacteria blooms have been observed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".